Gomez-Bombarelli et al. [1]} use deep generative models to generate new SMILES (Simplified Molecular Input Line Entry Specification) strings.
Kusner et al. [2]} extend such generative algorithms by imposing syntactic and semantic constraints on the decoder.
While generating valid structures,
the SMILES representation is inherently unable to capture many of the physical invariances present in molecules
[3]}.
Furthermore, similar structures may have markedly different SMILES representations.
In order to mitigate the structural representation limitations of SMILES, recently the SELFIES (SELF-referencIng Embedded Strings) representation has been proposed [4]}. Using formal grammar, Krenn et al. derive a representation that is highly robust. Even a random arrangement of the symbols defined in the grammar result in a valid molecule. This is a significant characteristic, as generative models benefit greatly from such a robust representation.
While using string representations is very attractive for generative modeling, they suffer from some drawbacks.
Other classes of materials such as periodic crystals also pose difficulties for SMILES and SELFIES.
Whilst future work can enable these character-based representations for a wider variety of materials, currently, these representations are limited to molecules [5]}.
Yao et al. developed RFCode, a string representation of crystals based on SMILES and InChI for reticular frameworks such as metal-organic frameworks (MOFs). Reticular frameworks are nanoporous crystals with many constituent atoms and use in diverse fields such as gas storage, separation, and catalysis [6]}. The authors designed a data-driven inverse design framework to propose new MOFs for optimized methane storage using a VAE.
